| title | titleSuffix | description | services | ms.service | ms.subservice | ms.topic | ms.custom | ms.author | author | ms.reviewer | ms.date |
|---|---|---|---|---|---|---|---|---|---|---|---|
Create compute clusters |
Azure Machine Learning |
Learn how to create compute clusters in your Azure Machine Learning workspace. Use the compute cluster as a compute target for training or inference. |
machine-learning |
machine-learning |
compute |
how-to |
devx-track-azurecli, cliv2, sdkv2, build-2023 |
vijetaj |
vijetajo |
sgilley |
05/03/2024 |
[!INCLUDE dev v2]
This article explains how to create and manage a compute cluster in your Azure Machine Learning workspace.
You can use Azure Machine Learning compute cluster to distribute a training or batch inference process across a cluster of CPU or GPU compute nodes in the cloud. For more information on the VM sizes that include GPUs, see GPU-optimized virtual machine sizes.
Learn how to:
- Create a compute cluster.
- Lower your compute cluster cost with low priority VMs.
- Set up a managed identity for the cluster.
[!INCLUDE serverless compute]
- An Azure Machine Learning workspace. For more information, see Manage Azure Machine Learning workspaces.
Select the appropriate tab for the rest of the prerequisites based on your preferred method of creating the compute cluster.
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If you're not running your code on a compute instance, install the Azure Machine Learning Python SDK. This SDK is already installed for you on a compute instance.
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Attach to the workspace in your Python script:
[!INCLUDE connect ws v2]
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If you're not running these commands on a compute instance, install the Azure CLI extension for Machine Learning service (v2). This extension is already installed for you on a compute instance.
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Authenticate and set the default workspace and resource group. Leave the terminal open to run the rest of the commands in this article.
[!INCLUDE cli first steps]
Start at Azure Machine Learning studio.
Azure Machine Learning compute cluster is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute cluster is a resource that can be shared with other users in your workspace. The compute scales up automatically when a job is submitted, and can be put in an Azure Virtual Network. Compute cluster supports no public IP deployment as well in virtual network. The compute executes in a containerized environment and packages your model dependencies in a Docker container.
Compute clusters can run jobs securely in either a managed virtual network or an Azure virtual network, without requiring enterprises to open up SSH ports. The job executes in a containerized environment and packages your model dependencies in a Docker container.
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Compute clusters can be created in a different region than your workspace. This functionality is only available for compute clusters, not compute instances.
[!WARNING] When using a compute cluster in a different region than your workspace or datastores, you might see increased network latency and data transfer costs. The latency and costs can occur when creating the cluster, and when running jobs on it.
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Azure Machine Learning Compute has default limits, such as the number of cores that can be allocated. For more information, see Manage and request quotas for Azure resources.
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Azure allows you to place locks on resources, so that they can't be deleted or are read only. Do not apply resource locks to the resource group that contains your workspace. Applying a lock to the resource group that contains your workspace prevents scaling operations for Azure Machine Learning compute clusters. For more information on locking resources, see Lock resources to prevent unexpected changes.
Time estimate: Approximately five minutes.
Note
If you use serverless compute, you don't need to create a compute cluster.
Azure Machine Learning Compute can be reused across runs. The compute can be shared with other users in the workspace and is retained between runs, automatically scaling nodes up or down based on the number of runs submitted, and the max_nodes set on your cluster. The min_nodes setting controls the minimum nodes available.
The dedicated cores per region per VM family quota and total regional quota, which applies to compute cluster creation, is unified and shared with Azure Machine Learning training compute instance quota.
[!INCLUDE min-nodes-note]
The compute autoscales down to zero nodes when it isn't used. Dedicated VMs are created to run your jobs as needed.
Use the following examples to create a compute cluster:
To create a persistent Azure Machine Learning Compute resource in Python, specify the size and max_instances properties. Azure Machine Learning then uses smart defaults for the other properties.
- size: The VM family of the nodes created by Azure Machine Learning Compute.
- max_instances: The maximum number of nodes to autoscale up to when you run a job on Azure Machine Learning Compute.
[!INCLUDE sdk v2]
You can also configure several advanced properties when you create Azure Machine Learning Compute. The properties allow you to create a persistent cluster of fixed size, or within an existing Azure Virtual Network in your subscription. See the AmlCompute class for details.
Warning
When setting the location parameter, if it's a different region than your workspace or datastores, you might see increased network latency and data transfer costs. The latency and costs can occur when creating the cluster, and when running jobs on it.
[!INCLUDE cli v2]
az ml compute create -f create-cluster.yml
Where the file create-cluster.yml is:
:::code language="yaml" source="~/azureml-examples-main/cli/resources/compute/cluster-location.yml":::
Warning
When you use a compute cluster in a different region than your workspace or datastores, you might see increased network latency and data transfer costs. The latency and costs can occur when creating the cluster, and when running jobs on it.
Create a single- or multi- node compute cluster for your training, batch inference or reinforcement learning workloads.
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Navigate to Azure Machine Learning studio.
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Under Manage, select Compute.
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If you have no compute resources, select New in the middle of the page.
:::image type="content" source="media/how-to-create-attach-studio/create-compute-target.png" alt-text="Screenshot that shows the New button to create a compute target.":::
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If you see a list of compute resources, select +New above the list.
:::image type="content" source="media/how-to-create-attach-studio/select-new.png" alt-text="Screenshot that shows the New button to create the resource.":::
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In the tabs at the top, select Compute cluster.
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Fill out the form as follows:
Field Description Location The Azure region where the compute cluster is created. By default, this is the same location as the workspace. If you don't have sufficient quota in the default region, switch to a different region for more options.
When using a different region than your workspace or datastores, you might see increased network latency and data transfer costs. The latency and costs can occur when creating the cluster, and when running jobs on it.Virtual machine type Choose CPU or GPU. This type can't be changed after creation. Virtual machine priority Choose Dedicated or Low priority. Low priority virtual machines are cheaper but don't guarantee the compute nodes. Your job might be preempted. Virtual machine size Supported virtual machine sizes might be restricted in your region. Check the availability list -
Select Next to proceed to Advanced Settings and fill out the form as follows:
Field Description Compute name * Name is required and must be between 3 to 24 characters long.
* Valid characters are upper and lower case letters, digits, and the - character.
* Name must start with a letter.
* Name needs to be unique across all existing computes within an Azure region. You see an alert if the name you choose isn't unique.
* If - character is used, then it needs to be followed by at least one letter later in the name.Minimum number of nodes Minimum number of nodes that you want to provision. If you want a dedicated number of nodes, set that count here. Save money by setting the minimum to 0, so you don't pay for any nodes when the cluster is idle. Maximum number of nodes Maximum number of nodes that you want to provision. The compute automatically scales to a maximum of this node count when a job is submitted. Idle seconds before scale down Idle time before scaling the cluster down to the minimum node count. Enable SSH access Use the same instructions as Enable SSH access for a compute instance. Advanced settings Optional. Configure network settings.
* If an Azure Virtual Network, Specify the Resource group, Virtual network, and Subnet to create the compute instance inside the network. For more information, see network requirements.
* If an Azure Machine Learning managed network, the compute cluster is automatically in the managed network. For more information, see managed computes with a managed network.
* No public IP configures whether the compute cluster has a public IP address when in a network.
* Assign a managed identity to grant access to resources. -
Select Create.
SSH access is disabled by default. SSH access can't be changed after creation. Make sure to enable access if you plan to debug interactively with VS Code Remote.
[!INCLUDE enable-ssh]
[!INCLUDE ssh-access]
You can also choose to use low-priority VMs to run some or all of your workloads. These VMs don't have guaranteed availability and might be preempted while in use. You have to restart a preempted job.
Using Azure Low Priority Virtual Machines allows you to take advantage of Azure's unused capacity at a significant cost savings. At any point in time when Azure needs the capacity back, the Azure infrastructure evicts Azure Low Priority Virtual Machines. Therefore, Azure Low Priority Virtual Machine is great for workloads that can handle interruptions. The amount of available capacity can vary based on size, region, time of day, and more. When deploying Azure Low Priority Virtual Machines, Azure allocates the VMs if there's capacity available, but there's no SLA for these VMs. An Azure Low Priority Virtual Machine offers no high availability guarantees. At any point in time when Azure needs the capacity back, the Azure infrastructure evicts Azure Low Priority Virtual Machines.
Use any of these ways to specify a low-priority VM:
[!INCLUDE sdk v2]
[!INCLUDE cli v2]
Set the vm-priority:
az ml compute create -f create-cluster.yml
Where the file create-cluster.yml is:
:::code language="yaml" source="~/azureml-examples-main/cli/resources/compute/cluster-low-priority.yml":::
Note
If you use serverless compute, you don't need to create a compute cluster. To specify a low-priority serverless compute, set the job_tier to Spot in the queue settings.
In the studio, choose Low Priority when you create a VM.
While your compute cluster scales down to zero nodes when not in use, unprovisioned nodes contribute to your quota usage. Deleting the compute cluster removes the compute target from your workspace, and releases the quota.
[!INCLUDE sdk v2]
This deletes the basic compute cluster, created from the create_basic object earlier in this article.
[!INCLUDE cli v2]
This deletes a compute cluster named basic-example.
az ml compute delete --name basic-example
- Navigate to Azure Machine Learning studio.
- In the left menu, under Manage, select Compute.
- At the top of the Compute page, select Compute cluster.
- Select the cluster you want to delete.
- At the top of the page, select Delete.
For information on how to configure a managed identity with your compute cluster, see Set up authentication between Azure Machine Learning and other services.
There's a chance that some users who created their Azure Machine Learning workspace from the Azure portal before the GA release might not be able to create AmlCompute in that workspace. You can either raise a support request against the service or create a new workspace through the portal or the SDK to unblock yourself immediately.
[!INCLUDE retiring vms]
If your Azure Machine Learning compute cluster appears stuck at resizing (0 -> 0) for the node state, Azure resource locks might be the cause.
[!INCLUDE resource locks]
Use your compute cluster to: